Journal
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
Volume 35, Issue 7-8, Pages 515-530Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compmedimag.2011.02.006
Keywords
Computational pathology; Machine learning; Medical imaging; Survival statistics; Cancer research; Whole slide imaging
Funding
- EU [213250]
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The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning. (C) 2011 Elsevier Ltd. All rights reserved.
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